Shape description is an important and fundamental issue in content-based image retrieval (CBIR), and a
number of shape description methods have been reported in the literature. For shape description, both global
information and local contour variations play important roles. In this paper a new included-angular ternary
pattern (IATP) based shape descriptor is proposed for shape image retrieval. For each point on the shape
contour, IATP is derived from its neighbor points, and IATP has good properties for shape description. IATP
is intrinsically invariant to rotation, translation and scaling. To enhance the description capability, multiscale
IATP histogram is presented to describe both local and global information of shape. Then multiscale IATP
histogram is combined with included-angular histogram for efficient shape retrieval. In the matching stage,
cosine distance is used to measure shape features’ similarity. Image retrieval experiments are conducted on the
standard MPEG-7 shape database and Swedish leaf database. And the shape image retrieval performance of the
proposed method is compared with other shape descriptors using the standard evaluation method. The
experimental results of shape retrieval indicate that the proposed method reaches higher precision at the same
recall value compared with other description method.

To combat the adverse impact imposed by illumination variation in the face recognition process, an effective and feasible algorithm is proposed in this paper. Firstly, an enhanced local texture feature is presented by applying the central symmetric encode principle on the fused component images acquired from the wavelet decomposition. Then the proposed local texture features are combined with Deep Belief Network (DBN) to gain robust deep features of face images under severe illumination conditions. Abundant experiments with different test schemes are conducted on both CMU-PIE and Extended Yale-B databases which contain face images under various illumination condition. Compared with the DBN, LBP combined with DBN and CSLBP combined with DBN, our proposed method achieves the most satisfying recognition rate regardless of the database used, the test scheme adopted or the illumination condition encountered, especially for the face recognition under severe illumination variation.

Intelligent human identification using face information has been the research hotspot ranging from Internet
of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent
access control. Since 2D face images are usually captured from a long distance in an unconstrained environment,
to fully exploit this advantage and make human recognition appropriate for wider intelligent applications
with higher security and convenience, the key difficulties here include gray scale change caused by
illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by
pose or expression variation. To conquer these, many solutions have been proposed. However, most of them
only improve recognition performance under one influence factor, which still cannot meet the real face
recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture
to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted
on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed
algorithm exhibits excellent discriminative ability compared with other existing algorithms.

Indexing

JIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

This journal was supported by the Korean Federation of Science and Technology Societies Grant funded by the Korean Government (Ministry of Education).

Society

ABOUT THE SOCIETY

Ever since information processing became one of the most important industries in the country, computing professionals have encountered a growing number of challenges.
Along with scholars and colleagues in related fields, they have gathered together at a variety of forums and meetings over the last few decades to share their knowledge and experiences,
and the outcomes of their research. These exchanges led to the founding of the Korea Information Processing Society (KIPS) on January 15, 1993. The KIPS was registered as an incorporated association under the Ministry of Science,
ICT and Future Planning under the government of the Republic of Korea. The main purpose of the KIPS organization is to improve our society by achieving the highest capability possible in the domain of information technology.
As such, it focuses on close collaboration with the nationâs industry, academic, and research communities to foster technological innovation,
to enhance its members' careers, and to promote the advanced information processing industry.